Diabetes mellitus is a collection of metabolic diseases characterized by hyperglycemia resulting from inadequate insulin secretion, insulin action, or both. Diabetes manifests itself differently in each person because of each person's unique physiology that interacts with variable health and lifestyle factors such as diet, weight, stress, illness, exercise, and medication intake. Biomarkers are patient biologically derived indicators of biological or pathogenic processes, pharmacologic responses, events or conditions (e.g., aging, disease or illness risk, presence or progression, etc.). For example, a biomarker can be an objective measurement of a variable related to a disease, which may serve as an indicator or predictor of that disease. In the case of diabetes mellitus, such biomarkers include measured values for glucose, lipids, triglycerides, and the like. A biomarker can also be a set of parameters from which to infer the presence or risk of a disease, rather than a measured value of the disease itself. When properly collected and evaluated, biomarkers can provide useful information related to a medical question about the patient, used as part of a medical assessment, as a medical control, and/or for medical optimization.
For diabetes, clinicians generally treat patients according to therapeutic guidelines such as Joslin Diabetes Center & Joslin Clinic, Clinical Guideline for Pharmacological Management of Type 2 Diabetes (2007) and Joslin Diabetes Center & Joslin Clinic, Clinical Guideline for Adults with Diabetes (2008). The guidelines may specify a desired biomarker value, e.g., a fasting blood glucose value of less than 100 mg/dl.
While guidelines and algorithms have been developed for insulin titration, the exit criterion for titration algorithms is often defined with the same threshold value applied to all patients. However, some biomarker levels (e.g. blood glucose measurements) have high variance, or noise associated with their measurements. Noise or variance may vary from patient to patient. The sources of variance, or noise, can be placed in two categories: system noise and protocol noise. In essence, system noise occurs when the amount of insulin delivered to the diabetic patient differs from the amount actually effective. System noise may be caused by insulin sensitivity i.e., variable physiological effects which vary the effectiveness of insulin from day to day. The protocol noise may result from patient error due to improper physical manipulation of the insulin delivery vehicle e.g., syringe or failure to measure blood glucose at the proper time. All sources of noise can lead to greater risk of adverse events (e.g., hyperglycemic and hypoglycemic events), and system noise in particular causes increased risk due to the difficulty in controlling the internal physiological effects. Consequently, patients with high system noise associated with their biomarker readings should not have the same exit criterion as patients with low levels of system noise
It is desirable to include an algorithm which accommodates for system noise in the exit criterion, thereby leading to fewer adverse events.